Overview

Dataset statistics

Number of variables16
Number of observations1600
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory200.1 KiB
Average record size in memory128.1 B

Variable types

Numeric11
Categorical5

Alerts

snow has constant value ""Constant
day_of_week is highly overall correlated with weekdayHigh correlation
dew is highly overall correlated with summertime and 1 other fieldsHigh correlation
hour_of_day is highly overall correlated with increase_stockHigh correlation
humidity is highly overall correlated with visibilityHigh correlation
increase_stock is highly overall correlated with hour_of_dayHigh correlation
month is highly overall correlated with summertimeHigh correlation
summertime is highly overall correlated with dew and 2 other fieldsHigh correlation
temp is highly overall correlated with dew and 1 other fieldsHigh correlation
visibility is highly overall correlated with humidityHigh correlation
weekday is highly overall correlated with day_of_weekHigh correlation
holiday is highly imbalanced (79.0%)Imbalance
hour_of_day has 84 (5.2%) zerosZeros
day_of_week has 240 (15.0%) zerosZeros
precip has 1445 (90.3%) zerosZeros
snowdepth has 1542 (96.4%) zerosZeros
windspeed has 66 (4.1%) zerosZeros
cloudcover has 122 (7.6%) zerosZeros

Reproduction

Analysis started2023-12-11 12:14:33.505375
Analysis finished2023-12-11 12:14:54.147822
Duration20.64 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

hour_of_day
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.37125
Minimum0
Maximum23
Zeros84
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-11T13:14:54.244252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median12
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9483702
Coefficient of variation (CV)0.61104718
Kurtosis-1.1768923
Mean11.37125
Median Absolute Deviation (MAD)6
Skewness0.012422467
Sum18194
Variance48.279848
MonotonicityNot monotonic
2023-12-11T13:14:54.476140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 84
 
5.2%
16 83
 
5.2%
13 78
 
4.9%
7 77
 
4.8%
23 77
 
4.8%
17 72
 
4.5%
21 71
 
4.4%
14 70
 
4.4%
3 69
 
4.3%
4 69
 
4.3%
Other values (14) 850
53.1%
ValueCountFrequency (%)
0 84
5.2%
1 67
4.2%
2 53
3.3%
3 69
4.3%
4 69
4.3%
5 67
4.2%
6 62
3.9%
7 77
4.8%
8 66
4.1%
9 63
3.9%
ValueCountFrequency (%)
23 77
4.8%
22 56
3.5%
21 71
4.4%
20 55
3.4%
19 57
3.6%
18 53
3.3%
17 72
4.5%
16 83
5.2%
15 64
4.0%
14 70
4.4%

day_of_week
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0225
Minimum0
Maximum6
Zeros240
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-11T13:14:54.605808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0129645
Coefficient of variation (CV)0.66599323
Kurtosis-1.2567133
Mean3.0225
Median Absolute Deviation (MAD)2
Skewness-0.037260916
Sum4836
Variance4.0520263
MonotonicityNot monotonic
2023-12-11T13:14:54.731270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 242
15.1%
0 240
15.0%
5 233
14.6%
6 231
14.4%
2 223
13.9%
3 220
13.8%
1 211
13.2%
ValueCountFrequency (%)
0 240
15.0%
1 211
13.2%
2 223
13.9%
3 220
13.8%
4 242
15.1%
5 233
14.6%
6 231
14.4%
ValueCountFrequency (%)
6 231
14.4%
5 233
14.6%
4 242
15.1%
3 220
13.8%
2 223
13.9%
1 211
13.2%
0 240
15.0%

month
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.46875
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-11T13:14:54.874859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4547406
Coefficient of variation (CV)0.53406619
Kurtosis-1.2202893
Mean6.46875
Median Absolute Deviation (MAD)3
Skewness0.040239033
Sum10350
Variance11.935233
MonotonicityNot monotonic
2023-12-11T13:14:54.997960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 147
9.2%
11 141
8.8%
4 140
8.8%
6 137
8.6%
7 136
8.5%
12 136
8.5%
5 133
8.3%
9 131
8.2%
2 131
8.2%
1 128
8.0%
Other values (2) 240
15.0%
ValueCountFrequency (%)
1 128
8.0%
2 131
8.2%
3 147
9.2%
4 140
8.8%
5 133
8.3%
6 137
8.6%
7 136
8.5%
8 121
7.6%
9 131
8.2%
10 119
7.4%
ValueCountFrequency (%)
12 136
8.5%
11 141
8.8%
10 119
7.4%
9 131
8.2%
8 121
7.6%
7 136
8.5%
6 137
8.6%
5 133
8.3%
4 140
8.8%
3 147
9.2%

holiday
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
0
1547 
1
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1600
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1547
96.7%
1 53
 
3.3%

Length

2023-12-11T13:14:55.146881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:14:55.286393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1547
96.7%
1 53
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 1547
96.7%
1 53
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1547
96.7%
1 53
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1547
96.7%
1 53
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1547
96.7%
1 53
 
3.3%

weekday
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
1
1136 
0
464 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1600
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1136
71.0%
0 464
29.0%

Length

2023-12-11T13:14:55.431196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:14:55.566785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1136
71.0%
0 464
29.0%

Most occurring characters

ValueCountFrequency (%)
1 1136
71.0%
0 464
29.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1136
71.0%
0 464
29.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1136
71.0%
0 464
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1136
71.0%
0 464
29.0%

summertime
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
1
1030 
0
570 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1600
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1030
64.4%
0 570
35.6%

Length

2023-12-11T13:14:55.698860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:14:55.872003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1030
64.4%
0 570
35.6%

Most occurring characters

ValueCountFrequency (%)
1 1030
64.4%
0 570
35.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1030
64.4%
0 570
35.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1030
64.4%
0 570
35.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1030
64.4%
0 570
35.6%

temp
Real number (ℝ)

HIGH CORRELATION 

Distinct343
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.210313
Minimum-9.1
Maximum35.6
Zeros4
Zeros (%)0.2%
Negative65
Negative (%)4.1%
Memory size12.6 KiB
2023-12-11T13:14:56.264453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-9.1
5-th percentile0.6
Q17.7
median15.5
Q323.2
95-th percentile28.9
Maximum35.6
Range44.7
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.2647847
Coefficient of variation (CV)0.60911205
Kurtosis-0.94665909
Mean15.210313
Median Absolute Deviation (MAD)7.7
Skewness-0.086542005
Sum24336.5
Variance85.836235
MonotonicityNot monotonic
2023-12-11T13:14:56.437152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.2 19
 
1.2%
4.9 19
 
1.2%
22.7 18
 
1.1%
5.5 18
 
1.1%
21.6 18
 
1.1%
23.7 17
 
1.1%
7.8 17
 
1.1%
9.3 16
 
1.0%
21 15
 
0.9%
20.5 14
 
0.9%
Other values (333) 1429
89.3%
ValueCountFrequency (%)
-9.1 1
 
0.1%
-8.4 4
0.2%
-7.2 1
 
0.1%
-5.3 1
 
0.1%
-4.9 1
 
0.1%
-4.5 2
0.1%
-3.9 4
0.2%
-3.6 1
 
0.1%
-3.4 3
0.2%
-3.1 1
 
0.1%
ValueCountFrequency (%)
35.6 1
 
0.1%
35.5 2
0.1%
34.9 1
 
0.1%
34.8 2
0.1%
34.4 1
 
0.1%
34.3 1
 
0.1%
33.9 1
 
0.1%
33.8 1
 
0.1%
33.7 4
0.2%
33.4 1
 
0.1%

dew
Real number (ℝ)

HIGH CORRELATION 

Distinct313
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.75075
Minimum-18.4
Maximum24.3
Zeros13
Zeros (%)0.8%
Negative425
Negative (%)26.6%
Memory size12.6 KiB
2023-12-11T13:14:56.665241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-18.4
5-th percentile-8.3
Q1-0.8
median8.3
Q316.8
95-th percentile22.1
Maximum24.3
Range42.7
Interquartile range (IQR)17.6

Descriptive statistics

Standard deviation10.026459
Coefficient of variation (CV)1.2936115
Kurtosis-0.99816486
Mean7.75075
Median Absolute Deviation (MAD)8.9
Skewness-0.23157023
Sum12401.2
Variance100.52988
MonotonicityNot monotonic
2023-12-11T13:14:56.846026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 20
 
1.2%
22.8 20
 
1.2%
20.7 20
 
1.2%
21.1 19
 
1.2%
22.1 18
 
1.1%
17.9 17
 
1.1%
1.1 17
 
1.1%
18.8 16
 
1.0%
11.1 16
 
1.0%
19.9 16
 
1.0%
Other values (303) 1421
88.8%
ValueCountFrequency (%)
-18.4 3
0.2%
-17.8 1
 
0.1%
-17.3 1
 
0.1%
-17.2 1
 
0.1%
-16.7 1
 
0.1%
-15.6 1
 
0.1%
-15.2 1
 
0.1%
-15 2
0.1%
-14.6 1
 
0.1%
-14.4 4
0.2%
ValueCountFrequency (%)
24.3 2
 
0.1%
24.2 2
 
0.1%
23.9 2
 
0.1%
23.8 1
 
0.1%
23.7 1
 
0.1%
23.6 1
 
0.1%
23.3 8
0.5%
23.2 3
 
0.2%
23.1 2
 
0.1%
23 1
 
0.1%

humidity
Real number (ℝ)

HIGH CORRELATION 

Distinct1431
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.927844
Minimum15.85
Maximum99.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-11T13:14:57.053709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum15.85
5-th percentile32.7865
Q147.845
median65.175
Q379.955
95-th percentile91.261
Maximum99.89
Range84.04
Interquartile range (IQR)32.11

Descriptive statistics

Standard deviation19.079419
Coefficient of variation (CV)0.29845242
Kurtosis-0.96007355
Mean63.927844
Median Absolute Deviation (MAD)15.96
Skewness-0.20954517
Sum102284.55
Variance364.02424
MonotonicityNot monotonic
2023-12-11T13:14:57.232618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.7 4
 
0.2%
57.11 3
 
0.2%
31.55 3
 
0.2%
49.58 3
 
0.2%
90.05 3
 
0.2%
90.04 3
 
0.2%
73.2 3
 
0.2%
60.65 3
 
0.2%
78.17 3
 
0.2%
88.49 3
 
0.2%
Other values (1421) 1569
98.1%
ValueCountFrequency (%)
15.85 1
0.1%
16.86 1
0.1%
17.03 1
0.1%
17.85 1
0.1%
18.71 1
0.1%
18.87 1
0.1%
19.66 1
0.1%
19.83 1
0.1%
20.28 1
0.1%
20.55 1
0.1%
ValueCountFrequency (%)
99.89 2
0.1%
99.85 1
0.1%
97.1 1
0.1%
96.98 1
0.1%
96.9 1
0.1%
96.85 1
0.1%
96.83 1
0.1%
96.68 1
0.1%
96.58 1
0.1%
96.21 1
0.1%

precip
Real number (ℝ)

ZEROS 

Distinct126
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12204188
Minimum0
Maximum25.871
Zeros1445
Zeros (%)90.3%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-11T13:14:57.417272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.338
Maximum25.871
Range25.871
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.92059968
Coefficient of variation (CV)7.5433099
Kurtosis411.54507
Mean0.12204188
Median Absolute Deviation (MAD)0
Skewness17.227086
Sum195.267
Variance0.84750377
MonotonicityNot monotonic
2023-12-11T13:14:57.621682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1445
90.3%
0.022 9
 
0.6%
0.018 5
 
0.3%
0.011 5
 
0.3%
0.278 4
 
0.2%
0.267 4
 
0.2%
0.006 3
 
0.2%
0.946 2
 
0.1%
0.036 2
 
0.1%
0.143 2
 
0.1%
Other values (116) 119
 
7.4%
ValueCountFrequency (%)
0 1445
90.3%
0.006 3
 
0.2%
0.008 1
 
0.1%
0.009 1
 
0.1%
0.011 5
 
0.3%
0.012 1
 
0.1%
0.014 1
 
0.1%
0.015 1
 
0.1%
0.018 5
 
0.3%
0.019 1
 
0.1%
ValueCountFrequency (%)
25.871 1
0.1%
11.133 1
0.1%
8.847 1
0.1%
8.046 1
0.1%
7.246 1
0.1%
7.08 1
0.1%
6.605 1
0.1%
5.172 1
0.1%
4.713 1
0.1%
4.551 1
0.1%

snow
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
0
1600 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1600
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1600
100.0%

Length

2023-12-11T13:14:57.788245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:14:57.935062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1600
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1600
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1600
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1600
100.0%

snowdepth
Real number (ℝ)

ZEROS 

Distinct41
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0427125
Minimum0
Maximum6.71
Zeros1542
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-11T13:14:58.071115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6.71
Range6.71
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42119757
Coefficient of variation (CV)9.861225
Kurtosis171.2107
Mean0.0427125
Median Absolute Deviation (MAD)0
Skewness12.675098
Sum68.34
Variance0.1774074
MonotonicityNot monotonic
2023-12-11T13:14:58.235167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 1542
96.4%
0.03 5
 
0.3%
0.1 5
 
0.3%
0.3 4
 
0.2%
0.05 3
 
0.2%
0.4 2
 
0.1%
0.2 2
 
0.1%
0.11 2
 
0.1%
0.02 2
 
0.1%
0.01 2
 
0.1%
Other values (31) 31
 
1.9%
ValueCountFrequency (%)
0 1542
96.4%
0.01 2
 
0.1%
0.02 2
 
0.1%
0.03 5
 
0.3%
0.04 1
 
0.1%
0.05 3
 
0.2%
0.08 1
 
0.1%
0.1 5
 
0.3%
0.11 2
 
0.1%
0.15 1
 
0.1%
ValueCountFrequency (%)
6.71 1
0.1%
6.68 1
0.1%
6.13 1
0.1%
5.64 1
0.1%
5.43 1
0.1%
5.23 1
0.1%
4.94 1
0.1%
3.87 1
0.1%
2.29 1
0.1%
2.24 1
0.1%

windspeed
Real number (ℝ)

ZEROS 

Distinct281
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.0825
Minimum0
Maximum43.8
Zeros66
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-11T13:14:58.460375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q17.5
median12.3
Q317.6
95-th percentile27.5
Maximum43.8
Range43.8
Interquartile range (IQR)10.1

Descriptive statistics

Standard deviation7.7566518
Coefficient of variation (CV)0.59290287
Kurtosis0.45909076
Mean13.0825
Median Absolute Deviation (MAD)4.9
Skewness0.6326675
Sum20932
Variance60.165647
MonotonicityNot monotonic
2023-12-11T13:14:58.651104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66
 
4.1%
4.8 39
 
2.4%
6.7 39
 
2.4%
7.1 27
 
1.7%
5.2 26
 
1.6%
7.4 25
 
1.6%
12.6 23
 
1.4%
5.4 21
 
1.3%
12.4 21
 
1.3%
9.2 21
 
1.3%
Other values (271) 1292
80.8%
ValueCountFrequency (%)
0 66
4.1%
0.1 7
 
0.4%
0.2 4
 
0.2%
0.3 5
 
0.3%
0.4 18
 
1.1%
0.5 16
 
1.0%
0.6 2
 
0.1%
0.7 5
 
0.3%
0.8 5
 
0.3%
0.9 2
 
0.1%
ValueCountFrequency (%)
43.8 1
0.1%
43.7 1
0.1%
40.6 1
0.1%
38.9 1
0.1%
38.2 1
0.1%
38 1
0.1%
37.4 1
0.1%
36.8 1
0.1%
36.4 1
0.1%
36.3 1
0.1%

cloudcover
Real number (ℝ)

ZEROS 

Distinct232
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.322375
Minimum0
Maximum100
Zeros122
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-11T13:14:58.862977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128.8
median79.3
Q392.8
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)64

Descriptive statistics

Standard deviation32.748869
Coefficient of variation (CV)0.50913651
Kurtosis-1.0931825
Mean64.322375
Median Absolute Deviation (MAD)20.7
Skewness-0.56158862
Sum102915.8
Variance1072.4884
MonotonicityNot monotonic
2023-12-11T13:14:59.068961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.4 200
 
12.5%
100 183
 
11.4%
79.3 137
 
8.6%
0 122
 
7.6%
44.6 93
 
5.8%
89.1 41
 
2.6%
25.3 31
 
1.9%
98.8 30
 
1.9%
99.6 28
 
1.8%
81.3 26
 
1.6%
Other values (222) 709
44.3%
ValueCountFrequency (%)
0 122
7.6%
1 1
 
0.1%
3.2 1
 
0.1%
20.8 1
 
0.1%
23.5 1
 
0.1%
24.4 200
12.5%
24.8 1
 
0.1%
25.3 31
 
1.9%
25.4 1
 
0.1%
25.8 3
 
0.2%
ValueCountFrequency (%)
100 183
11.4%
99.8 7
 
0.4%
99.6 28
 
1.8%
99.5 5
 
0.3%
99.3 3
 
0.2%
99.2 23
 
1.4%
99.1 1
 
0.1%
99 3
 
0.2%
98.8 30
 
1.9%
98.7 1
 
0.1%

visibility
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.344125
Minimum0.1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-11T13:14:59.284427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile10.5
Q116
median16
Q316
95-th percentile16
Maximum16
Range15.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3237368
Coefficient of variation (CV)0.15144147
Kurtosis17.094028
Mean15.344125
Median Absolute Deviation (MAD)0
Skewness-4.1150978
Sum24550.6
Variance5.3997528
MonotonicityNot monotonic
2023-12-11T13:14:59.482416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1351
84.4%
15.9 40
 
2.5%
14.2 20
 
1.2%
15.8 18
 
1.1%
15.6 13
 
0.8%
13.3 9
 
0.6%
11.5 8
 
0.5%
15.7 8
 
0.5%
15.5 5
 
0.3%
10.7 5
 
0.3%
Other values (73) 123
 
7.7%
ValueCountFrequency (%)
0.1 3
0.2%
1 1
 
0.1%
2 1
 
0.1%
2.3 2
0.1%
2.4 1
 
0.1%
2.5 2
0.1%
2.6 1
 
0.1%
3.1 3
0.2%
3.2 1
 
0.1%
3.3 1
 
0.1%
ValueCountFrequency (%)
16 1351
84.4%
15.9 40
 
2.5%
15.8 18
 
1.1%
15.7 8
 
0.5%
15.6 13
 
0.8%
15.5 5
 
0.3%
15.4 3
 
0.2%
15.3 2
 
0.1%
15.2 3
 
0.2%
15 2
 
0.1%

increase_stock
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
low_bike_demand
1312 
high_bike_demand
288 

Length

Max length16
Median length15
Mean length15.18
Min length15

Characters and Unicode

Total characters24288
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlow_bike_demand
2nd rowlow_bike_demand
3rd rowlow_bike_demand
4th rowlow_bike_demand
5th rowlow_bike_demand

Common Values

ValueCountFrequency (%)
low_bike_demand 1312
82.0%
high_bike_demand 288
 
18.0%

Length

2023-12-11T13:14:59.657530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:14:59.839588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
low_bike_demand 1312
82.0%
high_bike_demand 288
 
18.0%

Most occurring characters

ValueCountFrequency (%)
_ 3200
13.2%
e 3200
13.2%
d 3200
13.2%
i 1888
7.8%
b 1600
6.6%
k 1600
6.6%
m 1600
6.6%
a 1600
6.6%
n 1600
6.6%
l 1312
 
5.4%
Other values (4) 3488
14.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21088
86.8%
Connector Punctuation 3200
 
13.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3200
15.2%
d 3200
15.2%
i 1888
9.0%
b 1600
7.6%
k 1600
7.6%
m 1600
7.6%
a 1600
7.6%
n 1600
7.6%
l 1312
6.2%
o 1312
6.2%
Other values (3) 2176
10.3%
Connector Punctuation
ValueCountFrequency (%)
_ 3200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21088
86.8%
Common 3200
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3200
15.2%
d 3200
15.2%
i 1888
9.0%
b 1600
7.6%
k 1600
7.6%
m 1600
7.6%
a 1600
7.6%
n 1600
7.6%
l 1312
6.2%
o 1312
6.2%
Other values (3) 2176
10.3%
Common
ValueCountFrequency (%)
_ 3200
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 3200
13.2%
e 3200
13.2%
d 3200
13.2%
i 1888
7.8%
b 1600
6.6%
k 1600
6.6%
m 1600
6.6%
a 1600
6.6%
n 1600
6.6%
l 1312
 
5.4%
Other values (4) 3488
14.4%

Interactions

2023-12-11T13:14:51.801689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:34.738510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:36.488746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:38.288592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:39.911767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:41.534250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:43.162482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:44.755588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:46.905188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:48.606401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:50.147303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:51.949734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:34.909648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:36.717407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:38.429243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:40.070183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:41.699385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:43.297872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:45.372687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:47.059093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:48.738940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:50.276983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:52.085796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:35.105932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:36.895060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:38.610677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:40.242635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:41.844154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:43.444449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:45.541018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:47.200498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:48.881061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:50.420542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:52.234228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:35.258691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:37.053145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:38.787126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:40.387031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:41.986974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:43.582730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:45.678192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:47.342584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:49.026311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:50.564875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:52.412822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:35.397866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:37.226245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:38.925870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:40.528643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:42.124340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:43.723673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:45.808194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:47.476286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:49.152107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:50.704544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:52.566946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:35.546232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:37.368048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:39.062063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:40.666143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:42.251606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:43.855208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:45.939725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:47.622072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:49.280480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:50.882359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:52.711145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:35.689956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:37.516742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:39.189193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:40.814989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:42.397293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:43.982716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:46.087158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:47.794903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:49.437930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:51.025194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:52.862734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:35.822950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:37.673588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:39.321841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:40.955913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:42.537529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:44.124538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:46.239498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:48.021418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:49.572235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:51.180226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:53.029598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:35.981013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:37.824442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:39.470825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:41.098781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:42.683106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:44.279788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:46.456256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:48.170048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:49.724626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:51.352118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:53.201325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:36.147240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:37.973504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:39.620955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:41.235182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:42.827176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:44.476922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:46.596519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:48.310340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:49.862948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:51.498782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:53.352653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:36.313297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:38.111681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:39.757729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:41.368111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:42.965867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:44.605999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:46.745739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:48.454939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:49.995884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-11T13:14:51.654485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-12-11T13:14:59.964973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
cloudcoverday_of_weekdewholidayhour_of_dayhumidityincrease_stockmonthprecipsnowdepthsummertimetempvisibilityweekdaywindspeed
cloudcover1.0000.0510.1000.000-0.0130.3780.167-0.0680.3490.0650.218-0.080-0.3170.0440.108
day_of_week0.0511.0000.0180.219-0.0040.0140.114-0.0330.0500.0060.0000.008-0.0090.9980.045
dew0.1000.0181.0000.063-0.0210.4730.1620.2730.143-0.2470.6630.873-0.1630.121-0.141
holiday0.0000.2190.0631.0000.013-0.0170.000-0.0180.032-0.0170.126-0.0590.0070.112-0.024
hour_of_day-0.013-0.004-0.0210.0131.000-0.3270.515-0.0160.044-0.0180.0000.1560.0710.0000.185
humidity0.3780.0140.473-0.017-0.3271.0000.3080.1610.379-0.0040.1330.010-0.5130.052-0.344
increase_stock0.1670.1140.1620.0000.5150.3081.000-0.0400.1110.0910.213-0.337-0.1540.112-0.125
month-0.068-0.0330.273-0.018-0.0160.161-0.0401.0000.013-0.2300.9330.215-0.0120.050-0.192
precip0.3490.0500.1430.0320.0440.3790.1110.0131.0000.0750.000-0.020-0.4880.0000.040
snowdepth0.0650.006-0.247-0.017-0.018-0.0040.091-0.2300.0751.0000.111-0.282-0.0950.0670.085
summertime0.2180.0000.6630.1260.0000.1330.2130.9330.0000.1111.0000.7080.0350.000-0.003
temp-0.0800.0080.873-0.0590.1560.010-0.3370.215-0.020-0.2820.7081.0000.0530.0590.009
visibility-0.317-0.009-0.1630.0070.071-0.513-0.154-0.012-0.488-0.0950.0350.0531.0000.0000.128
weekday0.0440.9980.1210.1120.0000.0520.1120.0500.0000.0670.0000.0590.0001.000-0.033
windspeed0.1080.045-0.141-0.0240.185-0.344-0.125-0.1920.0400.085-0.0030.0090.128-0.0331.000

Missing values

2023-12-11T13:14:53.583659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T13:14:54.001064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

hour_of_dayday_of_weekmonthholidayweekdaysummertimetempdewhumidityprecipsnowsnowdepthwindspeedcloudcovervisibilityincrease_stock
0551000-7.2-15.053.680.000.016.331.616.0low_bike_demand
12141010-1.3-12.840.970.000.023.985.716.0low_bike_demand
2213801126.921.873.390.000.00.081.116.0low_bike_demand
31610003.1-4.059.740.000.019.20.016.0low_bike_demand
4170301011.7-11.418.710.000.010.544.616.0low_bike_demand
5173301127.112.840.980.000.013.681.316.0high_bike_demand
6204701130.924.367.710.000.020.350.316.0low_bike_demand
701120104.9-1.762.460.000.021.5100.016.0low_bike_demand
81801101016.613.883.560.000.018.188.916.0low_bike_demand
9751000-4.5-12.255.140.000.012.424.416.0low_bike_demand
hour_of_dayday_of_weekmonthholidayweekdaysummertimetempdewhumidityprecipsnowsnowdepthwindspeedcloudcovervisibilityincrease_stock
15901640017.8-1.153.610.00000.020.779.316.0low_bike_demand
1591214701129.721.661.850.00000.012.624.416.0low_bike_demand
15928230104.40.274.630.00000.00.024.416.0low_bike_demand
15931321101012.22.852.700.00000.018.589.116.0low_bike_demand
1594226900123.219.278.170.00000.09.147.616.0low_bike_demand
159535600121.519.487.680.00000.010.624.416.0low_bike_demand
1596140601123.220.182.432.21700.09.892.110.4low_bike_demand
1597130301113.9-2.232.930.00002.018.279.316.0low_bike_demand
1598145300111.7-9.322.090.00000.05.824.416.0high_bike_demand
159922620004.21.784.111.08100.021.997.416.0low_bike_demand